Atliq Hotel Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "729e47f8",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "806b3e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7af457bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings = pd.read_csv(\"C:\\\\Users\\\\TUSHAR GUPTA\\\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\fact_bookings.csv\")\n",
    "df_date = pd.read_csv(\"C:\\\\Users\\\\TUSHAR GUPTA\\\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\dim_date.csv\")\n",
    "df_hotel = pd.read_csv(\"C:\\\\Users\\TUSHAR GUPTA\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\dim_hotels.csv\")\n",
    "df_rooms = pd.read_csv(\"C:\\\\Users\\\\TUSHAR GUPTA\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\dim_rooms.csv\")\n",
    "df_agg_bookings = pd.read_csv(\"C:\\\\Users\\\\TUSHAR GUPTA\\\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\fact_aggregated_bookings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2f620a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT11        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT13        16558     28-04-22      1/5/2022      4/5/2022   \n",
       "3  May012216558RT14        16558     28-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0       -3.0           RT1    direct online            1.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        2.0           RT1          logtrip            5.0    Checked Out   \n",
       "3       -2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "0              10010             10010  \n",
       "1               9100              3640  \n",
       "2            9100000              9100  \n",
       "3               9100              3640  \n",
       "4              10920             10920  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "654f4f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5c3a59d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT1', 'RT2', 'RT3', 'RT4'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.room_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "679abbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2caa8881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            55066\n",
       "makeyourtrip      26898\n",
       "logtrip           14756\n",
       "direct online     13379\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6755\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd31ec99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='booking_platform'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e97188d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3e5b2312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12313f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa57025b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6bd7ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Luxury      16\n",
       "Business     9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60cefb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Mumbai       8\n",
       "Hyderabad    6\n",
       "Bangalore    6\n",
       "Delhi        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1555db8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Atliq Grands', 'Atliq Exotica', 'Atliq City', 'Atliq Blu',\n",
       "       'Atliq Bay', 'Atliq Palace', 'Atliq Seasons'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.property_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05d0a5d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hotel.city.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a980d7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e758cf49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16559, 19562, 19563, 17558, 16558, 17560, 19558, 19560, 17561,\n",
       "       16560, 16561, 16562, 16563, 17559, 17562, 17563, 18558, 18559,\n",
       "       18561, 18562, 18563, 19559, 19561, 17564, 18560], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "555c61e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id\n",
       "16559    7338\n",
       "18562    7333\n",
       "16563    7211\n",
       "18560    6638\n",
       "18561    6458\n",
       "17563    6337\n",
       "17559    6142\n",
       "19560    6079\n",
       "17560    6013\n",
       "19562    5812\n",
       "19561    5736\n",
       "19563    5413\n",
       "18559    5256\n",
       "17561    5183\n",
       "17558    5053\n",
       "16562    4820\n",
       "18563    4737\n",
       "19559    4729\n",
       "16560    4693\n",
       "18558    4475\n",
       "16561    4418\n",
       "19558    4400\n",
       "17564    3982\n",
       "17562    3424\n",
       "16558    3153\n",
       "Name: successful_bookings, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby('property_id')['successful_bookings'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "adea2301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>100</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>19558</td>\n",
       "      <td>11-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>19560</td>\n",
       "      <td>2-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>123</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings > df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "399224d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.capacity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83a158c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <td>1-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>38</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>17558</td>\n",
       "      <td>2-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>17558</td>\n",
       "      <td>3-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>26</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>17558</td>\n",
       "      <td>4-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>17558</td>\n",
       "      <td>5-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>29</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>17558</td>\n",
       "      <td>27-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>22</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8828</th>\n",
       "      <td>17558</td>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>21</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>17558</td>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>23</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9028</th>\n",
       "      <td>17558</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>32</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>30</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "27          17558      1-May-22           RT2                   38      50.0\n",
       "128         17558      2-May-22           RT2                   27      50.0\n",
       "229         17558      3-May-22           RT2                   26      50.0\n",
       "328         17558      4-May-22           RT2                   27      50.0\n",
       "428         17558      5-May-22           RT2                   29      50.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "8728        17558     27-Jul-22           RT2                   22      50.0\n",
       "8828        17558     28-Jul-22           RT2                   21      50.0\n",
       "8928        17558     29-Jul-22           RT2                   23      50.0\n",
       "9028        17558     30-Jul-22           RT2                   32      50.0\n",
       "9128        17558     31-Jul-22           RT2                   30      50.0\n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.capacity==df_agg_bookings.capacity.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f07a0fa",
   "metadata": {},
   "source": [
    "# DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3bffce21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c345f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have to clean the no_guest column as guest values can never in negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "87a957c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17924</th>\n",
       "      <td>May122218559RT44</td>\n",
       "      <td>18559</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>20900</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18020</th>\n",
       "      <td>May122218561RT22</td>\n",
       "      <td>18561</td>\n",
       "      <td>8/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9000</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18119</th>\n",
       "      <td>May122218562RT311</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>16800</td>\n",
       "      <td>16800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18121</th>\n",
       "      <td>May122218562RT313</td>\n",
       "      <td>18562</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>14400</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56715</th>\n",
       "      <td>Jun082218562RT12</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/6/2022</td>\n",
       "      <td>8/6/2022</td>\n",
       "      <td>13-06-22</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119765</th>\n",
       "      <td>Jul202219560RT220</td>\n",
       "      <td>19560</td>\n",
       "      <td>19-07-22</td>\n",
       "      <td>20-07-22</td>\n",
       "      <td>22-07-22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>13500</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT11        16558     27-04-22      1/5/2022   \n",
       "3        May012216558RT14        16558     28-04-22      1/5/2022   \n",
       "17924    May122218559RT44        18559    12/5/2022     12/5/2022   \n",
       "18020    May122218561RT22        18561     8/5/2022     12/5/2022   \n",
       "18119   May122218562RT311        18562     5/5/2022     12/5/2022   \n",
       "18121   May122218562RT313        18562    10/5/2022     12/5/2022   \n",
       "56715    Jun082218562RT12        18562     5/6/2022      8/6/2022   \n",
       "119765  Jul202219560RT220        19560     19-07-22      20-07-22   \n",
       "134586   Jul312217564RT47        17564     30-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022       -3.0           RT1    direct online            1.0   \n",
       "3           2/5/2022       -2.0           RT1           others            NaN   \n",
       "17924       14-05-22      -10.0           RT4    direct online            NaN   \n",
       "18020       14-05-22      -12.0           RT2     makeyourtrip            NaN   \n",
       "18119       17-05-22       -6.0           RT3   direct offline            5.0   \n",
       "18121       17-05-22       -4.0           RT3    direct online            NaN   \n",
       "56715       13-06-22      -17.0           RT1           others            NaN   \n",
       "119765      22-07-22       -1.0           RT2           others            NaN   \n",
       "134586      1/8/2022       -4.0           RT4          logtrip            2.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "0         Checked Out              10010             10010  \n",
       "3           Cancelled               9100              3640  \n",
       "17924         No Show              20900             20900  \n",
       "18020       Cancelled               9000              3600  \n",
       "18119     Checked Out              16800             16800  \n",
       "18121       Cancelled              14400              5760  \n",
       "56715     Checked Out               6500              6500  \n",
       "119765    Checked Out              13500             13500  \n",
       "134586    Checked Out              38760             38760  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.no_guests<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d43e763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "244802d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rid the records which are less than 0 in no_guests column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0d29867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134584</th>\n",
       "      <td>Jul312217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134578 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "2         Checked Out            9100000              9100  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134578 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings=df_bookings[df_bookings.no_guests>0]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6ae021a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134578.000000</td>\n",
       "      <td>134578.000000</td>\n",
       "      <td>56679.000000</td>\n",
       "      <td>1.345780e+05</td>\n",
       "      <td>134578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.143315</td>\n",
       "      <td>2.036744</td>\n",
       "      <td>3.619048</td>\n",
       "      <td>1.537804e+04</td>\n",
       "      <td>12696.011822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.053454</td>\n",
       "      <td>1.031710</td>\n",
       "      <td>1.234970</td>\n",
       "      <td>9.304015e+04</td>\n",
       "      <td>6927.841641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134578.000000  134578.000000   56679.000000       1.345780e+05   \n",
       "mean    18061.143315       2.036744       3.619048       1.537804e+04   \n",
       "std      1093.053454       1.031710       1.234970       9.304015e+04   \n",
       "min     16558.000000       1.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134578.000000  \n",
       "mean       12696.011822  \n",
       "std         6927.841641  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9fed4a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d4366",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see value of revenue_generated columns seems to be outlier so below this we can check this whether it is outlier or not using 3 std deviation and if yes then we have to remove that records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e78ebc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 93040.15493143328)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg,std = df_bookings.revenue_generated.mean(),df_bookings.revenue_generated.std()\n",
    "avg,std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e729a39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294498.50173198653"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_limit = avg + 3* std\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8698928f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-263742.4278566132"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit = avg-3*std\n",
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7a37683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Any value below the lower_limit and value higher than higher_limit is outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d77dfd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [booking_id, property_id, booking_date, check_in_date, checkout_date, no_guests, room_category, booking_platform, ratings_given, booking_status, revenue_generated, revenue_realized]\n",
       "Index: []"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated<lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40a9f9cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so there in no records below lower limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "49aa8788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>May012216559RT32</td>\n",
       "      <td>16559</td>\n",
       "      <td>29-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>28560000</td>\n",
       "      <td>28560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>May012216562RT22</td>\n",
       "      <td>16562</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>12600000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>May012217559RT118</td>\n",
       "      <td>17559</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2000000</td>\n",
       "      <td>4420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129176</th>\n",
       "      <td>Jul282216562RT26</td>\n",
       "      <td>16562</td>\n",
       "      <td>21-07-22</td>\n",
       "      <td>28-07-22</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct online</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10000000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "111      May012216559RT32        16559     29-04-22      1/5/2022   \n",
       "315      May012216562RT22        16562     28-04-22      1/5/2022   \n",
       "562     May012217559RT118        17559     26-04-22      1/5/2022   \n",
       "129176   Jul282216562RT26        16562     21-07-22      28-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "111         2/5/2022        6.0           RT3    direct online            NaN   \n",
       "315         4/5/2022        2.0           RT2   direct offline            3.0   \n",
       "562         2/5/2022        2.0           RT1           others            NaN   \n",
       "129176      29-07-22        2.0           RT2    direct online            3.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "2         Checked Out            9100000              9100  \n",
       "111       Checked Out           28560000             28560  \n",
       "315       Checked Out           12600000             12600  \n",
       "562         Cancelled            2000000              4420  \n",
       "129176    Checked Out           10000000             12600  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated>=higher_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285a3bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so we find the outlier now we have to remove this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aaece2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>booking_id</th>\n",
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       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
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       "      <td>1/5/2022</td>\n",
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       "      <td>16558</td>\n",
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       "      <td>1/5/2022</td>\n",
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       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
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       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3640</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>134584</th>\n",
       "      <td>Jul312217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134573 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "7        May012216558RT18        16558     26-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "7           3/5/2022        2.0           RT1          logtrip            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "7             No Show               9100              9100  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134573 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings=df_bookings[df_bookings.revenue_generated<higher_limit]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2934ab9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134573, 12)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c0204526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_id               0\n",
       "property_id              0\n",
       "booking_date             0\n",
       "check_in_date            0\n",
       "checkout_date            0\n",
       "no_guests                0\n",
       "room_category            0\n",
       "booking_platform         0\n",
       "ratings_given        77897\n",
       "booking_status           0\n",
       "revenue_generated        0\n",
       "revenue_realized         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d165055",
   "metadata": {},
   "source": [
    "Total values in our dataframe is 134576. Out of that 77899 rows has null rating. Since there are many rows with null rating, we should not filter these values. Also we should not replace this rating with a median or mean rating etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c1d4b1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fd612bda",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22       NaN\n",
       "14        17562      1-May-22           RT1                   12       NaN"
      ]
     },
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     "metadata": {},
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   ],
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    "df_agg_bookings[df_agg_bookings.capacity.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7d116ac7",
   "metadata": {},
   "outputs": [
    {
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       "25.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
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   "source": [
    "df_agg_bookings.capacity.median()"
   ]
  },
  {
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   "execution_count": 45,
   "id": "a33ac518",
   "metadata": {},
   "outputs": [
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       "<p>9200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "0           16559      1-May-22           RT1                   25      30.0\n",
       "1           19562      1-May-22           RT1                   28      30.0\n",
       "2           19563      1-May-22           RT1                   23      30.0\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "4           16558      1-May-22           RT1                   18      19.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "9195        16563     31-Jul-22           RT4                   13      18.0\n",
       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
       "9198        19563     31-Jul-22           RT4                    3       6.0\n",
       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
       "[9200 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.capacity.fillna(df_agg_bookings.capacity.median(),inplace = True)\n",
    "df_agg_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3d3355a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a3cfb1b1",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1cbae980",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>9194 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "0           16559      1-May-22           RT1                   25      30.0\n",
       "1           19562      1-May-22           RT1                   28      30.0\n",
       "2           19563      1-May-22           RT1                   23      30.0\n",
       "4           16558      1-May-22           RT1                   18      19.0\n",
       "5           17560      1-May-22           RT1                   28      40.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "9195        16563     31-Jul-22           RT4                   13      18.0\n",
       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
       "9198        19563     31-Jul-22           RT4                    3       6.0\n",
       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
       "[9194 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings = df_agg_bookings[df_agg_bookings.successful_bookings <= df_agg_bookings.capacity]\n",
    "df_agg_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "73d1e1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194, 5)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdf9f8b",
   "metadata": {},
   "source": [
    "# DATA TRANSFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a58e518d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>30.0</td>\n",
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       "      <td>1-May-22</td>\n",
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       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "4        16558      1-May-22           RT1                   18      19.0\n",
       "5        17560      1-May-22           RT1                   28      40.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c7bd9ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg_bookings['occ_pct'] = df_agg_bookings.apply(lambda row: row['successful_bookings']/row['capacity'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4601fe6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "4        16558      1-May-22           RT1                   18      19.0   \n",
       "5        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "    occ_pct  \n",
       "0  0.833333  \n",
       "1  0.933333  \n",
       "2  0.766667  \n",
       "4  0.947368  \n",
       "5  0.700000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7149fad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings['occ_pct'] = df_agg_bookings['occ_pct'].apply(lambda x: round(x*100, 2))\n",
    "df_agg_bookings.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31cdeb1",
   "metadata": {},
   "source": [
    "# INSIGHTS GENERATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7855994",
   "metadata": {},
   "source": [
    "1. What is an average occupancy rate in each of the room categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7db93941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "4        16558      1-May-22           RT1                   18      19.0   \n",
       "5        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  \n",
       "4    94.74  \n",
       "5    70.00  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c6000a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    57.889643\n",
       "RT2    58.009756\n",
       "RT3    58.028213\n",
       "RT4    59.277925\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby('room_category')['occ_pct'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3dccd0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# which type of room is rt1 , rt2, rt3, rt4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ab15fe6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fc32265e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "3        16558      1-May-22           RT1                   18      19.0   \n",
       "4        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct room_id room_class  \n",
       "0    83.33     RT1   Standard  \n",
       "1    93.33     RT1   Standard  \n",
       "2    76.67     RT1   Standard  \n",
       "3    94.74     RT1   Standard  \n",
       "4    70.00     RT1   Standard  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_agg_bookings,df_rooms,left_on='room_category',right_on='room_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1257dfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_class\n",
       "Elite           58.009756\n",
       "Premium         58.028213\n",
       "Presidential    59.277925\n",
       "Standard        57.889643\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('room_class')['occ_pct'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "48f95fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='room_class'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('room_class')['occ_pct'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e173634e",
   "metadata": {},
   "source": [
    "3. Print average occupancy rate per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6543ac86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "3        16558      1-May-22           RT1                   18      19.0   \n",
       "4        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct room_id room_class  \n",
       "0    83.33     RT1   Standard  \n",
       "1    93.33     RT1   Standard  \n",
       "2    76.67     RT1   Standard  \n",
       "3    94.74     RT1   Standard  \n",
       "4    70.00     RT1   Standard  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ea8eb6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bebcd77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>2-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>3-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>17</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>4-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16559</td>\n",
       "      <td>5-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>16</td>\n",
       "      <td>30.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9189</th>\n",
       "      <td>16563</td>\n",
       "      <td>27-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>10</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.56</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9190</th>\n",
       "      <td>16563</td>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9191</th>\n",
       "      <td>16563</td>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9192</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9193</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9194 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559      1-May-22           RT1                   25      30.0   \n",
       "1           16559      2-May-22           RT1                   20      30.0   \n",
       "2           16559      3-May-22           RT1                   17      30.0   \n",
       "3           16559      4-May-22           RT1                   21      30.0   \n",
       "4           16559      5-May-22           RT1                   16      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "9189        16563     27-Jul-22           RT4                   10      18.0   \n",
       "9190        16563     28-Jul-22           RT4                    9      18.0   \n",
       "9191        16563     29-Jul-22           RT4                    9      18.0   \n",
       "9192        16563     30-Jul-22           RT4                   11      18.0   \n",
       "9193        16563     31-Jul-22           RT4                   13      18.0   \n",
       "\n",
       "      occ_pct room_id    room_class  property_name  category    city  \n",
       "0       83.33     RT1      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "1       66.67     RT1      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "2       56.67     RT1      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "3       70.00     RT1      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "4       53.33     RT1      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "...       ...     ...           ...            ...       ...     ...  \n",
       "9189    55.56     RT4  Presidential   Atliq Palace  Business   Delhi  \n",
       "9190    50.00     RT4  Presidential   Atliq Palace  Business   Delhi  \n",
       "9191    50.00     RT4  Presidential   Atliq Palace  Business   Delhi  \n",
       "9192    61.11     RT4  Presidential   Atliq Palace  Business   Delhi  \n",
       "9193    72.22     RT4  Presidential   Atliq Palace  Business   Delhi  \n",
       "\n",
       "[9194 rows x 11 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,df_hotel,on='property_id')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fda74e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.33\n",
       "Delhi        61.51\n",
       "Hyderabad    58.12\n",
       "Mumbai       57.91\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cccafb",
   "metadata": {},
   "source": [
    "3. In the month of June, what is the occupancy for different cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "42f2d855",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        16559      2-May-22           RT1                   20      30.0   \n",
       "2        16559      3-May-22           RT1                   17      30.0   \n",
       "3        16559      4-May-22           RT1                   21      30.0   \n",
       "4        16559      5-May-22           RT1                   16      30.0   \n",
       "\n",
       "   occ_pct room_id room_class  property_name category    city  \n",
       "0    83.33     RT1   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "1    66.67     RT1   Standard  Atliq Exotica   Luxury  Mumbai  \n",
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       "3    70.00     RT1   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "4    53.33     RT1   Standard  Atliq Exotica   Luxury  Mumbai  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fa5ce2f8",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 88,
     "metadata": {},
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    }
   ],
   "source": [
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "733eb0c6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "3        16559     10-May-22           RT4                   13      18.0   \n",
       "4        19562     10-May-22           RT1                   18      30.0   \n",
       "\n",
       "   occ_pct room_id    room_class  property_name category       city  \\\n",
       "0    60.00     RT1      Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1    60.98     RT2         Elite  Atliq Exotica   Luxury     Mumbai   \n",
       "2    62.50     RT3       Premium  Atliq Exotica   Luxury     Mumbai   \n",
       "3    72.22     RT4  Presidential  Atliq Exotica   Luxury     Mumbai   \n",
       "4    60.00     RT1      Standard      Atliq Bay   Luxury  Bangalore   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0  10-May-22  May 22    W 20  weekeday  \n",
       "1  10-May-22  May 22    W 20  weekeday  \n",
       "2  10-May-22  May 22    W 20  weekeday  \n",
       "3  10-May-22  May 22    W 20  weekeday  \n",
       "4  10-May-22  May 22    W 20  weekeday  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,df_date,left_on='check_in_date',right_on='date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8fd1c4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mmm yy'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "031aa8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>26</td>\n",
       "      <td>41.0</td>\n",
       "      <td>63.41</td>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0</td>\n",
       "      <td>63.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294</th>\n",
       "      <td>17558</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4295</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>27</td>\n",
       "      <td>41.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4296</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>22</td>\n",
       "      <td>38.0</td>\n",
       "      <td>57.89</td>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4297</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4298</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2099 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "2200        16559     10-Jun-22           RT1                   20      30.0   \n",
       "2201        16559     10-Jun-22           RT2                   26      41.0   \n",
       "2202        16559     10-Jun-22           RT3                   20      32.0   \n",
       "2203        16559     10-Jun-22           RT4                   11      18.0   \n",
       "2204        19562     10-Jun-22           RT1                   19      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "4294        17558     30-Jun-22           RT4                    3       6.0   \n",
       "4295        16563     30-Jun-22           RT1                   27      41.0   \n",
       "4296        16563     30-Jun-22           RT2                   22      38.0   \n",
       "4297        16563     30-Jun-22           RT3                   14      20.0   \n",
       "4298        16563     30-Jun-22           RT4                   12      18.0   \n",
       "\n",
       "      occ_pct room_id    room_class  property_name  category       city  \\\n",
       "2200    66.67     RT1      Standard  Atliq Exotica    Luxury     Mumbai   \n",
       "2201    63.41     RT2         Elite  Atliq Exotica    Luxury     Mumbai   \n",
       "2202    62.50     RT3       Premium  Atliq Exotica    Luxury     Mumbai   \n",
       "2203    61.11     RT4  Presidential  Atliq Exotica    Luxury     Mumbai   \n",
       "2204    63.33     RT1      Standard      Atliq Bay    Luxury  Bangalore   \n",
       "...       ...     ...           ...            ...       ...        ...   \n",
       "4294    50.00     RT4  Presidential   Atliq Grands    Luxury     Mumbai   \n",
       "4295    65.85     RT1      Standard   Atliq Palace  Business      Delhi   \n",
       "4296    57.89     RT2         Elite   Atliq Palace  Business      Delhi   \n",
       "4297    70.00     RT3       Premium   Atliq Palace  Business      Delhi   \n",
       "4298    66.67     RT4  Presidential   Atliq Palace  Business      Delhi   \n",
       "\n",
       "           date  mmm yy week no  day_type  \n",
       "2200  10-Jun-22  Jun 22    W 24  weekeday  \n",
       "2201  10-Jun-22  Jun 22    W 24  weekeday  \n",
       "2202  10-Jun-22  Jun 22    W 24  weekeday  \n",
       "2203  10-Jun-22  Jun 22    W 24  weekeday  \n",
       "2204  10-Jun-22  Jun 22    W 24  weekeday  \n",
       "...         ...     ...     ...       ...  \n",
       "4294  30-Jun-22  Jun 22    W 27  weekeday  \n",
       "4295  30-Jun-22  Jun 22    W 27  weekeday  \n",
       "4296  30-Jun-22  Jun 22    W 27  weekeday  \n",
       "4297  30-Jun-22  Jun 22    W 27  weekeday  \n",
       "4298  30-Jun-22  Jun 22    W 27  weekeday  \n",
       "\n",
       "[2099 rows x 15 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun_22 = df[df['mmm yy']=='Jun 22']\n",
    "df_jun_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1143d01d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Delhi        62.47\n",
       "Hyderabad    58.46\n",
       "Mumbai       58.38\n",
       "Bangalore    56.44\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun_22.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a38ef09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_jun_22.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending = False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c405d73",
   "metadata": {},
   "source": [
    "4. When was the occupancy better? Weekday or Weekend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2b1178a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>25</td>\n",
       "      <td>41.0</td>\n",
       "      <td>60.98</td>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "3        16559     10-May-22           RT4                   13      18.0   \n",
       "4        19562     10-May-22           RT1                   18      30.0   \n",
       "\n",
       "   occ_pct room_id    room_class  property_name category       city  \\\n",
       "0    60.00     RT1      Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1    60.98     RT2         Elite  Atliq Exotica   Luxury     Mumbai   \n",
       "2    62.50     RT3       Premium  Atliq Exotica   Luxury     Mumbai   \n",
       "3    72.22     RT4  Presidential  Atliq Exotica   Luxury     Mumbai   \n",
       "4    60.00     RT1      Standard      Atliq Bay   Luxury  Bangalore   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0  10-May-22  May 22    W 20  weekeday  \n",
       "1  10-May-22  May 22    W 20  weekeday  \n",
       "2  10-May-22  May 22    W 20  weekeday  \n",
       "3  10-May-22  May 22    W 20  weekeday  \n",
       "4  10-May-22  May 22    W 20  weekeday  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "13e207b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.88\n",
       "weekend     72.34\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('day_type')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9a30f97d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='occ_pct'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oGBicnWRH0TSWjIpcLTYXTFoeC4bIGvp39sMn8ffAmUc0iuE7qxJ5RRV46uOfWDBEVrTzzFW8svmY7BiaxpJRgYLSKoz4ZD/Sr5bKjkKkORuTs/Def0/LjqFZLBk7V1ljxLNrklgwRAr6cGc6Vv90UXYMTWLJ2LmXN6bi0MUC2TGINO/NLSfwzbEc2TE0hyVjx/6+4wy+SrksOwaRQzAJYNqGFBzkBn9WxZKxU5uSs7D0x3OyYxA5lMoaEyasOoSzV3gNmrWwZOzQT+nXMZszXoikKKqowZ+XH0TOjXLZUTSBJWNn0q+W4Lm1yagymmRHIXJYOTcq8OflB3GjrFp2FNVjydiR6yWVGLfiEG6U8y82kWxnr5TgL/9OhpE7zDYJS8ZOVFQb8czqJGTkl8mOQkQ/23vuOhbuOCM7hqqxZOzErE2pOJxRKDsGEf3G/+1Kx44TubJjqBZLxg5sOJTBqcpEdkoIYMYXR3HhGi+ItgRLRrJzeSV4c8tJ2TGI6DaKK2rw3JpklFcZZUdRHZaMRJU1Rkz57AjKq/kXl8jenblSjDlfHZcdQ3VYMhLN234ap7h1MpFqfJGchS+PZMuOoSosGUl+OHUFK/ddlB2DiBrp9S+P4yLHZxqMJSPBlaIKzNyYKjsGEVmgpLIGkz87jKoaXjDdECwZGzOZBKZvSEF+aZXsKERkoePZRZj3zSnZMVSBJWNjy3alY1/6ddkxiKiJVu67iEMXuWLznbBkbCj5UgEWf3dWdgwisgIhgNmbj/G02R2wZGykotqIFz9PQQ3XQSLSjHN5JfhwJ7fkuB2WjI28/0MaLl3numREWrMsIR1p3H/mllgyNnAmtxj/SjwvOwYRKaDKaMLszccgBM9S3AxLRmFCCMzenIpqI/8CEmlV0qUCrD2QITuGXWLJKGztgQyurkzkABZ8cxq5Nypkx7A7LBklleSh49lP4e7EtcmItK64soZrm90ES0ZJ381B7/Pv42jrtzA+KFN2GiJS2I6TV/Dt8RzZMeyKTnC0ShkZB4DlgwD8+vZmBD+GCTl/xNlSd3m5iEhRrZsZ8P2MOHi7uciOYhd4JKMEkwnYPgP/WzAA0C7ra/zXeTo+CjsIFz27nUiL8oorsSwhXXYMu8GSUULKOiD32E2/passwqNZS5AaOA9PtLli42BEZAsr915EXjEnAQAsGeurqQQS5t/xbu7XjuO9GzPwXfhmBLtV2iAYEdlKebURS39Ikx3DLrBkrO3gJ0BRVoPuqhMmhGduxG73mXg3NBU6HU+hEWnFhkOZyOAqHywZq6ooAhIXNfph+vJrGH55PlLbLsbDflyhmUgLqo0Cf//ujOwY0rFkrGnfUqDc8qW/m+UlYVnpdGwJ3w4/12orBiMiGbYcvYzTuY69xTpLxlpKrgI/fdTkp9GZatA9cy32e7+C10JOWyEYEcliEsB73zr20QxLxlp2LwCqrbfvt1NJDp7JfQuHOyzDA81vWO15ici2fjidh+RLjru5GUvGGgozgeSVijx1i5xErK2ahvXhP6KZc40ir0FEynrXgY9mWDLW8NMHgLFKsafXGSvRK/NfONxyDqa0u6DY6xCRMg5eyMfOM3myY0jBZWWaqiwfWNzVqqfK7iQ3cCCeu/YkUoq8bPaaRNQ0XYO88fWUWNkxbI5HMk118BObFgwAtLn8Hf4jpmN5+F646bnCM5EaHM8uwsELjjc2w5Jpiupy4ODHUl5aV12KAZkf4qj/O4gPvCwlAxE1zsp9jne6myXTFEfWAmXXpEYwFJzBW/kvYVfYeoR7lkvNQkS3t+PEFVwudKx/pywZS5mMwE8fyk5Rq33WFuxwfhFLw5LgpDPJjkNEN1FjEli7/5LsGDbFkrHUya+AAvs69NVV3sDgrL/jWNACDPN3zJksRPZu/aFMVFQ7zlgqS8ZS+5fJTnBLHtdSsajoRfw3/EsEcYVnIruSX1qFLSmOM47KkrFE3ikg66DsFLelEyZ0zvwciR4z8f863HxvGyKSY+W+i7Ij2AxLxhLJq2QnaDB92TWMzJmH1PZLMLCV402fJLJHJ3McZzozS6axaiqB1A2yUzSa95WD+LhsOr4M/xYtucIzkXSOMp2ZJdNYp7Y2aTl/mXSmavTIXI0DPq/ilfZnZcchcmiOMp2ZJdNYh9VzquxWnIuz8dyVN5Hc4Z/o6evYe10QyVJjEvjsYIbsGIpjyTRG/gXgQqLsFFbTMmcX1tdMw7/DE+Dp7DhTKonsxdaj2p9lxpJpjMOrAWhrPVFdTQV6Z36MlJZzMKntRdlxiBzKxetlOJ6t7f2iWDINJQSQ+rnsFIpxuXEBs66+ip86rkK3ZrZd8JPIkX2dmiM7gqJYMg2VlQQUZclOobiA7P9ii246Pg7bD4Oey9MQKW37MZYMAcDJL2UnsBldVQl+l7UUR9u8g1EB2j9nTCRTRn4ZUrMKZcdQDEumoU5tkZ3A5tzyT+OdgpnYGfY5Qj0qZMch0qxtGj5lxpJpiOzDQKH2pxrejA4CHbK+xPeuM7C442HodNqa+EBkD7Zp+JQZS6YhTn4lO4F0+ooC/DF7IY4Hv4c/tL4qOw6RpmQVlCMls1B2DEWwZBrCAU+V3Yrn1RQsLX4R34RvQYBblew4RJqxLVWb458smTvJPQbkn5edwq7ohBERmeuxx/NlvNXhhOw4RJqw/Viu7AiKYMncyentshPYLafSPMTnzMXR9kvRr0WB7DhEqpZdWI4jGdr7d8SSuZPzO2UnsHs+V/ZjRcU0bArfgeYuNbLjEKnW7rPXZEewOpbM7VSWmC/CpDvSmapxd+ZKHPJ9FS+1Pyc7DpEqHbhwXXYEq2PJ3M6lvYCJe680hnNxFiZfmYNDoZ/gPq7wTNQohzMKUFWjrZU2WDK3c36X7ASq5Xd5JzbUTMfa8F3wdNLWPxoipVRUmzQ3lZklczvnE2QnUDVdTTn6ZP4TR/zewLPBjnkxK1FjHTivrVNmLJlbKckD8k7KTqEJroXpePXaK9jbcQ0ivMpkxyGya/s1Ni7DkrmV87ugtb1jZAvK/gbbnabj/8IOwEXP95boZg5fKtTUuAxL5lYu7padQJN0lcV4OOt9HAuYi6cCtLteE5GlyquNmlqVmSVzK9mHZSfQNLfrJzGv4CX8ELYRIe5c4Znof+3X0LgMS+ZmqsuBvFOyU2ieDgIdszbjR7eXsDA0hSs8E/3swIV82RGshiVzMzmpgDDKTuEw9OX5eOLyAhwLXoRH/bR3xTNRYyVfKkC1URvjMiyZm7l8RHYCh+R19TA+LJmOr8O/RmsDL4Ilx1VWZcTJy9q4mJklczMsGWl0woiumf/GT16z8EYIT1mS4zpzpVh2BKtgydzMZQ76y+ZUmotxuW/jSMiH6NuiUHYcIps7l1ciO4JVsGR+q7IYuM4FHu1F89y9WFU5DV+Efw8frvBMDiSNRzIalZMKCG0MuGmFzliFezOXI7n5a5jejhvIkWNI45GMRl09LTsB3YJzUSZeyHsdB0P/hRgfbfyWR3Qr2YXlKK9S/yxXi0pm9erVqKysrHd7VVUVVq9e3eRQUnGrZbvX+vKP2GSajlXhiXB3Uv8/QqKbEUIb4zIWlcy4ceNw48aNercXFxdj3LhxTQ4l1fV02QmoAXTVZYjLXIajrd/C+KBM2XGIFJGWp/4jdotKRggBnU5X7/asrCz4+Pg0OZRU+SwZNXEtSMNfr89CYtg6dOEKz6QxWhiXcW7MnaOjo6HT6aDT6fDggw/C2fnXhxuNRly4cAEPP/yw1UPajMkIFFyUnYIs0DZrG74xJOKbsKfxwvl7UW2q/0sQkdqkXXGwkhk6dCgAICUlBYMGDYKXl1ft91xdXRESEoLHH3/cqgFtqjADMFbJTkEW0lUW4dGsJegf2BV/rRmPjbn+siMRNck5DZwu0wkhGr0q4apVq/DUU0/BYDAokUmec98Da1VcklRL6PQ4FzwM47MeRUa5m+w4RBZx0utw4m+D4ObiJDuKxSwak4mMjERKSkq92w8cOICkpKSmZpLnOmeWaYVOmBCeuREJbi/j3dBUrvBMqmQ0CWQVlMuO0SQWlczzzz+PzMz6M3qys7Px/PPPNzmUNIWXZCcgK9OXX8Pwy/OR2nYxHvbTzh4d5DjyS9V9Ct+ikjl58iRiYmLq3R4dHY2TJ082OZQ0xbmyE5BCmuUlYVnpdGwJ3w4/V67wTOqRX1r/mkQ1sahkDAYDrly5Uu/2nJycOjPOVKek/n8TaYfOVIPumWux3/sVvBbClR1IHa474pHMwIEDMXv27DoXZBYWFuLVV1/FwIEDrRbO5ngk4xCcSnLwTO5bOBzyEfq0qH9RMZE9yS9Rd8lYdNixaNEi9O3bF+3bt0d0dDQA87Rmf39/rFmzxqoBbao0T3YCsqEWuXuwxukQDoaPwYQLfVFco+KjcNKs/DJ1l4xFU5gBoLS0FOvWrcPRo0fh7u6O7t27Y8SIEXBxcbF2Rtsw1gBvt5SdgiSp9m6PpW7P4h8ZHWRHIapjSI9AvP9UtOwYFrO4ZDSn+AqwqJPsFCRZbuBAPHftSaQUed35zkQ2EBveCmvG95Qdw2IWnx84c+YM/vGPf+DUqVPQ6XTo0qULJk+ejC5dulgzn+2UcXorAW0uf4f/uOzDzvCxeP58L5Qb1XsRHGnDdZWPyVg08L9x40Z07doVycnJiIqKQvfu3XH48GF069YNX3zxhbUz2kZ5gewEZCd01aUYkPkhUlq/gz8HZsuOQw5O7dfJWHS6LDQ0FKNHj8Zbb71V5/Y33ngDa9aswfnzKrxyPu07YN0TslOQHboUPBgTcoYgrdRddhRyQK5Oepyd+4jsGBaz6EgmNzcX8fHx9W4fPXo0cnNVOg24pkJ2ArJT7bO2YIfzi1galgQnHbfmJtuqMppQXKHeC4gtKpl+/fohMTGx3u179uxBbGxsk0NJUaPuq2pJWbrKGxic9XccC1qAYf6c6k62VVxRIzuCxSwa+B88eDBmzZqF5ORk9OrVCwCwf/9+fPHFF/jb3/6GLVu21LmvKnCJf2oAj2upWKR7ERPDn8DTmY8gu0JjK5GTXTKa1DsJ2KIxGb2+YQdAOp0ORqNK9mBPWgF8PU12ClIRk0crrPd5Bq9e6CY7Cmlcwkv9ENLKU3YMi1h0usxkMjXoQzUFA/BIhhpNX3YNI3PmIbX9EgxslS87DmlYjYqPZCwqmYbq1q3bTbcEsEsckyELeV85iI/LpuPL8G/Rkis8kwJMKr5mXtGSuXjxIqqrVfKPzsiSIcvpTNXokbkaB3xexez2Z2XHIY2pMaq3ZLgi4C9MnJpKTedcnI2JxW9iXEA4jHpOCiDr0OuXA/CWHcMiLJlfOKl0YU+yS64FabIjkKao5IzQTSh6ukxVnPlbJxHZKb1619BjyfyCJUNE9kqn3h/V6k1ubU4sGSKyU3r1jmwoWjL//Oc/4e/vr+RLWI+zm+wEREQ3Z2gmO4HFLCqZqVOnYunSpfVu/+CDDzBt2rTar0eOHAlPT5VcpersKjsBEdHNGdQ5swywsGQ2bdqEBx54oN7tvXv3xsaNG5scSgoeyRCRPXJyBVzU+/PJopK5fv06fHx86t3u7e2Na9euNTmUFBz4JyJ7pOJTZYCFJRMWFoZvv/223u3ffPMNQkNDmxxKCjdf2QmIiOpT8akywMKLMV988UVMnjwZV69exYABAwAAP/zwAxYtWoQlS5ZYM5/teLWWnYCIqD43ByyZp59+GpWVlZg7dy7efvttAEBISAiWLVt20x0zVcHTT3YCIqL6VH4kY9F+Mv/r6tWrcHd3h5eXl7UyyTO/HVBxQ3YKIqJfdXkMeGqd7BQWs2hM5sKFC0hLM6/N5OfnV1swaWlpuHjxotXC2RyPZojI3ni2kp2gSSwqmbFjx2Lfvn31bj9w4ADGjh3b1EzyeHJchojsjHew7ARNYlHJHDly5KbXyfTq1QspKSlNzSSPyn9jICIN8nHAktHpdCguLq53+40bN9S15fJv8XQZEdkbRyyZ2NhYzJs3r06hGI1GzJs3D3369LFaOJvzDpCdgIioLp8g2QmaxKIpzAsWLEDfvn3RuXNnxMbGAgASExNRVFSEH3/80aoBbaqFSi8kJSKN0jnmmExkZCRSU1MxfPhw5OXlobi4GPHx8Th9+jS6du1q7Yy20zJMdgIiol95tVb94r0Wb1Lg4eGBFi1aICAgADqdDl5eXnByUu/ubQCAFh1lJyAi+pW3uk+VARYeySQlJaFjx45YvHgx8vPzce3aNSxevBgdO3bE4cOHrZ3RdgxegJdK9r8hIu3zbSc7QZNZdMV/bGwswsLC8Mknn8DZ2XwwVFNTgwkTJuD8+fPYvXu31YPazPJHgIz61wAREdlcv1eBfrNkp2gSi49kZs2aVVswAODs7IyXX34ZSUlJVgsnRUsO/hORnWgdITtBk1lUMt7e3sjIyKh3e2ZmJpo1U/feBxz8JyK74X+X7ARNZlHJDB8+HOPHj8eGDRuQmZmJrKwsrF+/HhMmTMCIESOsndG2WDJEZA9cPIDmHWSnaDKLZpctXLgQOp0O8fHxqKmpAQC4uLhg0qRJmD9/vlUD2lybbrITEBEBfp0BvUXHAXalSUv9l5WVIT09HUIIhIWFwcPDw5rZ5FkQCpRdl52CiBxZj1HA0I9kp2gyi6+TAczXynTrpsHf/AN6AOk/yE5BRI6sdaTsBFah/mMxJQTFyE5ARI7OnyWjXYHRshMQkUPTAYHa+GWXJXMzLBkikql1BODuKzuFVbBkbsY7EPBqIzsFETmqdr1kJ7Aalsyt8GiGiGRp11t2AqthydxK+/tlJyAiR8UjGQfQIU52AiJyRN7BgG9b2SmshiVzKwFRgHsL2SmIyNFo6CgGYMncmk4HdOgrOwURORqWjAMJ7Sc7ARE5Go2dqmfJ3A5LhohsqUUo4NdJdgqrYsncTosOgG972SmIyFGED5KdwOpYMnfCoxkispVOLBnHEz5QdgIicgSuzYD2D8hOYXUsmTsJewhw8ZSdgoi0rmM/wNlVdgqrY8nciYs7j2aISHmdHpadQBEsmYaIHCI7ARFpmk6Tg/4AS6ZhOg0CnN1lpyAirWrbE/Dyk51CESyZhnD1BMIelJ2CiLSq+5OyEyiGJdNQkUNlJyAiLdK7AHcNk51CMSyZhur8MOBkkJ2CiLQmfCDgod3FeFkyDWVoxllmRGR93f8kO4GiWDKN0WOU7AREpCUGH6DTI7JTKIol0xjhvwO8/GWnICKtiPwD4OImO4WiWDKN4eQMRD0lOwURaUV37f88Yck0VnS87AREpAW+7YGQPrJTKI4l01itwoCQWNkpiEjt7nvGvAOvxrFkLHHPONkJiEjNXDyB6DGyU9gES8YSEYMBT20uAUFENtD9T4C7r+wUNsGSsYSTCxDzZ9kpiEit7ntWdgKbYclYqudEwFnbUw+JSAEhsYB/pOwUNsOSsZRXa05nJqLG6/mc7AQ2xZJpit5TAR3fQiJqIJ92QGdtX+H/W/wJ2RQtOwJdHpOdgojUotckQO8kO4VNsWSa6oFpshMQkRp4tALuHis7hc2xZJoq+G6gvfav2iWiJrr/L4Crh+wUNseSsYY+02QnICJ75ubrUNOW/xdLxhrCBwKBMbJTEJG9uv95855UDoglYy0PvSk7ARHZI/fm5gF/B8WSsZbQOKDjANkpiMje9J7qsEcxAEvGuh58A4D2V1UlogbyaGVeHcSBsWSsKbAHcNcfZacgInvRfzbg6ik7hVQsGWsb8Dqgd5Gdgohka30XcDe3BWHJWFvLjkCMY+wTQUS3MWiuw13dfzMsGSXEvQK4eslOQUSydHoE6Nhfdgq7wJJRQjN/oN8rslMQkQx6F/NRDAFgySin5yTzOVkiciz3PWs+bU4AWDLKcXIGHvs7OKWZyIF4tATiXpadwq6wZJTUrhfQY5TsFERkKw+9Cbj7yk5hV1gyShv4lnlZCSLStg5xQEy87BR2hyWjNM+WP68EQESa5eIJDF4qO4VdYsnYwt1jgeB7ZacgIqUMeB1oHiI7hV1iydiCTgcM+QhwdpedhIisLfg+oOdzslPYLZaMrfh14nYARFrjZACGfADo+aP0VvjO2FLPiUBoP9kpiMha+s4E/DrLTmHXWDK29MtpMzcf2UmIqKkCenDr9QZgydiaTxDw6CLZKYioKVybAU+uAJy44vqdsGRk6P4kcNcw2SmIyFJ/WAK0CJWdQhVYMrI89negWaDsFETUWNGjgW5PyE6hGiwZWdybmw+3ucEZkXq06gw88p7sFKrCkpGpXS/gd2/LTkFEDeHsZv7F0NVDdhJVYcnI1msS0JWH3kR2b9BcwJ/bdzQWS8YeDF4K+EXITkFEt9LtT8C9E2SnUCWWjD1w9QSGrwUM3rKTENFvBd0DDP6H7BSqxZKxF63CgKHLwE3OiOyIdxDw1L8BFzfZSVSLJWNPIh4DYmfITkFEgHlB26fWAc38ZSdRNZaMvRnwOicCENmDoR8BgdGyU6geS8be6HTm02YhsbKTEDmuvi8DXbkqhzWwZOyRs6t5IgBnnBHZXuQQoP+rslNoBkvGXrn7AqO+ALzayE5C5Dg69AWGfWI+o0BWwZKxZ75tzUXj6iU7CZH2BcYAT30GOBtkJ9EUloy9C+gO/GkVoHeWnYRIu1p1BkZvAgz8hc7aWDJqEPbQz4fwTrKTEGmPbzsg/kvAo4XsJJrEklGLrsOAYR+zaIisybM1MOZLwJvbbiiFJaMm3Z4wT2/W8X8bUZO5+QBjNgMtO8pOomn8aaU2UcOBIR+yaIiawr25+QimTTfZSTSPo8lq1GMkYKoBtkwFIGSnIVIXz9bmMRgu228TLBm1iokHTEbg6+lg0RA1ULNA4M9bgFbhspM4DJ0Qgj+h1OzIOmDLFEAYZSchsm++7c0F0zxEdhKHwpLRgtPbgI1PAzUVspMQ2aeW4eaC4Swym2PJaMXFPcBnI4DKItlJiOxL67uA+K8ALz/ZSRwSS0ZLco8D654AinNkJyGyDx0HAE+uNE9XJilYMlpzIwtY+zhw9bTsJERy3T0OeHQh4MT5TTKxZLSovBBYPwq4tEd2EiLb0+mBgW8BvafITkJgyWiXsRr49hXg0L9kJyGyHRcP8zp/EY/JTkI/Y8lo3eHVwLaXAGOl7CREyvJqA4xczy2T7QxLxhFkJQEbRnNCAGlXYLR5N1mfYNlJ6DdYMo6i+Arw+Rgg84DsJETWdfc44JF3udmYnWLJOJKaKuCbmUDyStlJiJrO2R14bDHQY4TsJHQbLBlHdHS9eZymqlh2EiLLtOpsvv7FP1J2EroDloyjyr8AbH4WyDooOwlR4/QYZb7+xdVDdhJqAJaMIzPWALsXALsXcoFNsn8GH+DRBUDUU7KTUCOwZAjI2A9sfgYozJCdhOjmOj4IDP4H4BMkOwk1EkuGzCpuANtmAMe+kJ2E6FeuzYBB7wB3j5WdhCzEkqG6Tm0Ftr8MFF+WnYQcXWg/YPAHgG9b2UmoCVgyVF9FEfDj2+YlaYRJdhpyNK5e5rXH7h0vOwlZAUuGbi0rCdj6AnDluOwk5Cg6PWK+sLJ5e9lJyEpYMnR7xhrgpw+AXe8C1WWy05BWtehoLpfwgbKTkJWxZKhhCi4C37wCnP1GdhLSElcvoO9LQK/nAWdX2WlIASwZapwLicCO14GcFNlJSO26PWkee/EOlJ2EFMSSocYTwjzV+Ye3gRu8toYaKSAKGDQPCHlAdhKyAZYMWa6mEjjwf0DiIvN1NkS30zoS6DcbiPgDoNPJTkM2wpKhpivLB3a/ByQtB2oqZKche9Oio7lcuj4O6PWy05CNsWTIekryzDPRDi3nCs8E+LYD4mYBUSMAvZPsNCQJS4asr7wAOPCx+VRaeb7sNGRrzTsAvacAMfGAk4vsNCQZS4aUU1VqPoW27wOgJFd2GlJau/uB+58HOv+ep8WoFkuGlFdTad4o7dC/gNxU2WnImvTOQORQc7kExchOQ3aIJUO2lZUEHPoUOLGZkwTUzM0HiPkz0PM5Lr9Pt8WSITnK8oGUf5tPp+Wny05DDaIDOsQCPUabpyFzZ0pqAJYMySUEcD4BOLwaOPst10ezR81DgKiRQI8R5hljRI3AkiH7UVUKnPkGOL4JOPc9YKySnchxuXgCdw0FeowE2j/AiyfJYiwZsk/lhcDpr82Fc2E3YKqRnUj73FsAnR4Guvwe6DiAp8PIKlgyZP9Kr5l37EzbAZzfBVSXyk6kHb7tzFOOu/weaN+bF02S1bFkSF1qKoGLe4C074DzO4Grp2UnUhcnVyDobvPWxp0fBQK6y05EGseSIXUrumyeOHA+Acg8YN73hn71S6mE9DF/tO0JuLjLTkUOhCVD2lJyFcg69OvH5SNAVYnsVLbj3hwI6AEE32teSp+lQpKxZEjbTCYg76S5cK6cAK6dAa6e1cYyNz5tAf+7zEvoB0QBgT3M042J7AhLhhxTxQ1z2Vw7+2vx5KcDRTn2s4K0Tg94BwG+7c3l0bz9r5+37mK+6p7IzrFkiH6rsgQozjUf7RTnAsU5P/+ZC1QWAVVl5hluVaX/83kZYKys/1x6Z0DnZP5T72yeveXmbT6t5d7C/KfHz3/+cpuXn7lMfNpy33tSPZYMkbWYjObZb3qnXwuFyMGxZIiISDHc9IHuaOXKlfD19bX567755pvo0aOHzV+XiKyHJUNERIphyRARkWJYMiq0detW+Pr6wmQyAQBSUlKg0+kwc+bM2vtMnDgRI0aMAADs27cPffv2hbu7O9q2bYupU6eitPTX9b+qqqrw8ssvIygoCJ6enujZsycSEhJu+frXr1/Hfffdh8GDB6OiogJCCCxYsAChoaFwd3dHVFQUNm7cWHv/hIQE6HQ6/PDDD7jnnnvg4eGB3r1748yZM3Wed/78+fD390ezZs0wfvx4VFRwUzMitWPJqFDfvn1RXFyMI0eOAAB27dqFVq1aYdeuXbX3SUhIQFxcHI4dO4ZBgwZh2LBhSE1NxYYNG7Bnzx5Mnjy59r7jxo3D3r17sX79eqSmpuLJJ5/Eww8/jLS0tHqvnZWVhdjYWHTp0gWbN2+Gm5sbXn/9daxYsQLLli3DiRMnMH36dIwePbpOHgB47bXXsGjRIiQlJcHZ2RlPP/107fc+//xzvPHGG5g7dy6SkpIQEBCAjz76yNpvHRHZmiBViomJEQsXLhRCCDF06FAxd+5c4erqKoqKikROTo4AIE6dOiXGjBkjnn322TqPTUxMFHq9XpSXl4tz584JnU4nsrOz69znwQcfFLNnzxZCCLFixQrh4+Mjzpw5I9q1ayemTJkiTCaTEEKIkpIS4ebmJvbt21fn8ePHjxcjRowQQgixc+dOAUB8//33td/ftm2bACDKy8uFEELcf//94rnnnqvzHD179hRRUVFNfKeISCYeyahUv379kJCQACEEEhMTMWTIEHTt2hV79uzBzp074e/vjy5duiA5ORkrV66El5dX7cegQYNgMplw4cIFHD58GEIIdOrUqc59du3ahfT0X7dFLi8vR58+fTB06FAsXboUup83sTp58iQqKiowcODAOo9fvXp1nccDQPfuv674GxAQAADIy8sDAJw6dQr3339/nfv/9msiUh9n2QHIMv369cOnn36Ko0ePQq/XIzIyEnFxcdi1axcKCgoQFxcHADCZTJg4cSKmTp1a7znatWuH1NRUODk5ITk5GU5OdS8e9PLyqv3cYDDgoYcewrZt2zBz5kwEBwfXPj8AbNu2DUFBQXUebzAY6nzt4uJS+/kvJfXL44lIm1gyKvXLuMySJUsQFxcHnU6HuLg4zJs3DwUFBXjhhRcAADExMThx4gTCwsJu+jzR0dEwGo3Iy8tDbGzsLV9Pr9djzZo1GDlyJAYMGICEhAQEBgYiMjISBoMBGRkZtcVmiYiICOzfvx/x8fG1t+3fv9/i5yMi+8DTZSrl4+ODHj16YO3atejXrx8Ac/EcPnwYZ8+erb1t1qxZ+Omnn/D8888jJSUFaWlp2LJlC6ZMmQIA6NSpE0aNGoX4+Hhs3rwZFy5cwKFDh/Duu+9i+/btdV7TyckJ69atQ1RUFAYMGIDc3Fw0a9YML730EqZPn45Vq1YhPT0dR44cwYcffohVq1Y1+L/nhRdewPLly7F8+XKcPXsWb7zxBk6cOGGV94qI5GHJqFj//v1hNBprC6V58+aIjIyEn58fIiIiAJjHQXbt2oW0tDTExsYiOjoaf/3rX2vHRABgxYoViI+Px4wZM9C5c2cMHjwYBw4cQNu2beu9prOzMz777DPcddddGDBgAPLy8vD2229jzpw5mDdvHiIiIjBo0CBs3boVHTp0aPB/y/DhwzFnzhzMmjULd999Ny5duoRJkyY17Q0iIum4dhkRESmGRzJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDBERKYYlQ0REimHJEBGRYlgyRESkGJYMEREphiVDRESKYckQEZFiWDJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDBERKYYlQ0REimHJEBGRYlgyRESkGJYMEREphiVDRESKYckQEZFiWDJERKQYlgwRESmGJUNERIphyRARkWJYMkREpBiWDBERKYYlQ0REimHJEBGRYlgyRESkmP8PkB4h2WjOPR8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('day_type')['occ_pct'].mean().round(2).plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8525f8f",
   "metadata": {},
   "source": [
    "5. We got new data for the month of august. Append that to existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "776c254e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>room_category</th>\n",
       "      <th>room_class</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category       city room_category room_class  \\\n",
       "0        16559  Atliq Exotica    Luxury     Mumbai           RT1   Standard   \n",
       "1        19562      Atliq Bay    Luxury  Bangalore           RT1   Standard   \n",
       "2        19563   Atliq Palace  Business  Bangalore           RT1   Standard   \n",
       "3        19558   Atliq Grands    Luxury  Bangalore           RT1   Standard   \n",
       "4        19560     Atliq City  Business  Bangalore           RT1   Standard   \n",
       "\n",
       "  check_in_date  mmm yy week no  day_type  successful_bookings  capacity  \\\n",
       "0     01-Aug-22  Aug-22    W 32  weekeday                   30        30   \n",
       "1     01-Aug-22  Aug-22    W 32  weekeday                   21        30   \n",
       "2     01-Aug-22  Aug-22    W 32  weekeday                   23        30   \n",
       "3     01-Aug-22  Aug-22    W 32  weekeday                   30        40   \n",
       "4     01-Aug-22  Aug-22    W 32  weekeday                   20        26   \n",
       "\n",
       "     occ%  \n",
       "0  100.00  \n",
       "1   70.00  \n",
       "2   76.67  \n",
       "3   75.00  \n",
       "4   76.92  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august = pd.read_csv(\"C:\\\\Users\\\\TUSHAR GUPTA\\\\Desktop\\\\Python\\\\source-code\\\\3_project_hospitality_analysis\\\\datasets\\\\new_data_august.csv\")\n",
    "df_august.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "32d41de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>25</td>\n",
       "      <td>41.0</td>\n",
       "      <td>60.98</td>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "3        16559     10-May-22           RT4                   13      18.0   \n",
       "4        19562     10-May-22           RT1                   18      30.0   \n",
       "\n",
       "   occ_pct room_id    room_class  property_name category       city  \\\n",
       "0    60.00     RT1      Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1    60.98     RT2         Elite  Atliq Exotica   Luxury     Mumbai   \n",
       "2    62.50     RT3       Premium  Atliq Exotica   Luxury     Mumbai   \n",
       "3    72.22     RT4  Presidential  Atliq Exotica   Luxury     Mumbai   \n",
       "4    60.00     RT1      Standard      Atliq Bay   Luxury  Bangalore   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0  10-May-22  May 22    W 20  weekeday  \n",
       "1  10-May-22  May 22    W 20  weekeday  \n",
       "2  10-May-22  May 22    W 20  weekeday  \n",
       "3  10-May-22  May 22    W 20  weekeday  \n",
       "4  10-May-22  May 22    W 20  weekeday  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c5c8d3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'property_name', 'category', 'city', 'room_category',\n",
       "       'room_class', 'check_in_date', 'mmm yy', 'week no', 'day_type',\n",
       "       'successful_bookings', 'capacity', 'occ%'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ee79b220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'check_in_date', 'room_category', 'successful_bookings',\n",
       "       'capacity', 'occ_pct', 'room_id', 'room_class', 'property_name',\n",
       "       'category', 'city', 'date', 'mmm yy', 'week no', 'day_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ac484e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "676d9194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 15)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7d8f0112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "      <th>property_name</th>\n",
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       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
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       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
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       "      <td>25</td>\n",
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       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "3        16559     10-May-22           RT4                   13      18.0   \n",
       "4        19562     10-May-22           RT1                   18      30.0   \n",
       "\n",
       "   occ_pct room_id    room_class  property_name category       city  \\\n",
       "0    60.00     RT1      Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1    60.98     RT2         Elite  Atliq Exotica   Luxury     Mumbai   \n",
       "2    62.50     RT3       Premium  Atliq Exotica   Luxury     Mumbai   \n",
       "3    72.22     RT4  Presidential  Atliq Exotica   Luxury     Mumbai   \n",
       "4    60.00     RT1      Standard      Atliq Bay   Luxury  Bangalore   \n",
       "\n",
       "        date  mmm yy week no  day_type  occ%  \n",
       "0  10-May-22  May 22    W 20  weekeday   NaN  \n",
       "1  10-May-22  May 22    W 20  weekeday   NaN  \n",
       "2  10-May-22  May 22    W 20  weekeday   NaN  \n",
       "3  10-May-22  May 22    W 20  weekeday   NaN  \n",
       "4  10-May-22  May 22    W 20  weekeday   NaN  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df = pd.concat([df,df_august], ignore_index=False)\n",
    "latest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "df244490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6504, 16)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b68c666",
   "metadata": {},
   "source": [
    "6. Print revenue realized per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "547ac106",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "54cce0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "eec02e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings = pd.merge(df_bookings,df_hotel,on='property_id')\n",
    "df_updated_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "411c9b06",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Mumbai       668569251\n",
       "Bangalore    420383550\n",
       "Hyderabad    325179310\n",
       "Delhi        294404488\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('city')['revenue_realized'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "23432221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('city')['revenue_realized'].sum().sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b64968",
   "metadata": {},
   "source": [
    "7. Print month by month revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c6d09ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "04f063df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>checkout_date</th>\n",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
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       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
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       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
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     "execution_count": 131,
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   "source": [
    "df_updated_bookings.head()"
   ]
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   "cell_type": "code",
   "execution_count": 123,
   "id": "f84a56e2",
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134573 entries, 0 to 134572\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   booking_id         134573 non-null  object \n",
      " 1   property_id        134573 non-null  int64  \n",
      " 2   booking_date       134573 non-null  object \n",
      " 3   check_in_date      134573 non-null  object \n",
      " 4   checkout_date      134573 non-null  object \n",
      " 5   no_guests          134573 non-null  float64\n",
      " 6   room_category      134573 non-null  object \n",
      " 7   booking_platform   134573 non-null  object \n",
      " 8   ratings_given      56676 non-null   float64\n",
      " 9   booking_status     134573 non-null  object \n",
      " 10  revenue_generated  134573 non-null  int64  \n",
      " 11  revenue_realized   134573 non-null  int64  \n",
      " 12  property_name      134573 non-null  object \n",
      " 13  category           134573 non-null  object \n",
      " 14  city               134573 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_updated_bookings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "13bf8c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   date      92 non-null     object\n",
      " 1   mmm yy    92 non-null     object\n",
      " 2   week no   92 non-null     object\n",
      " 3   day_type  92 non-null     object\n",
      "dtypes: object(4)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
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   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "84551d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date['date']=pd.to_datetime(df_date['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "316dfafb",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0 2022-05-10  May 22    W 19   weekend\n",
       "1 2022-05-10  May 22    W 19  weekeday\n",
       "2 2022-05-10  May 22    W 19  weekeday\n",
       "3 2022-05-10  May 22    W 19  weekeday\n",
       "4 2022-05-10  May 22    W 19  weekeday"
      ]
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     "execution_count": 135,
     "metadata": {},
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    "df_date.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "dcb9fb37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
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      " 2   week no   92 non-null     object        \n",
      " 3   day_type  92 non-null     object        \n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "9f89baec",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22    2022-05-01      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22    2022-05-01      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022    2022-05-01      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22    2022-05-01      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22    2022-05-01      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings['check_in_date'] = pd.to_datetime (df_updated_bookings['check_in_date'],format='mixed',dayfirst=True)\n",
    "df_updated_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "3c687ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134573 entries, 0 to 134572\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   booking_id         134573 non-null  object        \n",
      " 1   property_id        134573 non-null  int64         \n",
      " 2   booking_date       134573 non-null  object        \n",
      " 3   check_in_date      134573 non-null  datetime64[ns]\n",
      " 4   checkout_date      134573 non-null  object        \n",
      " 5   no_guests          134573 non-null  float64       \n",
      " 6   room_category      134573 non-null  object        \n",
      " 7   booking_platform   134573 non-null  object        \n",
      " 8   ratings_given      56676 non-null   float64       \n",
      " 9   booking_status     134573 non-null  object        \n",
      " 10  revenue_generated  134573 non-null  int64         \n",
      " 11  revenue_realized   134573 non-null  int64         \n",
      " 12  property_name      134573 non-null  object        \n",
      " 13  category           134573 non-null  object        \n",
      " 14  city               134573 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(9)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_updated_bookings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "88aa2e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "      <td>May102216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>19-04-22</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "      <td>May102216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>8/5/2022</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>15-05-22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "      <td>May102216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>11/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "      <td>May102216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>11/5/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "      <td>May102216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>9/5/2022</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>15-05-22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no day_type        booking_id  property_id  \\\n",
       "0 2022-05-10  May 22    W 19  weekend  May102216558RT11        16558   \n",
       "1 2022-05-10  May 22    W 19  weekend  May102216558RT12        16558   \n",
       "2 2022-05-10  May 22    W 19  weekend  May102216558RT13        16558   \n",
       "3 2022-05-10  May 22    W 19  weekend  May102216558RT14        16558   \n",
       "4 2022-05-10  May 22    W 19  weekend  May102216558RT15        16558   \n",
       "\n",
       "  booking_date check_in_date checkout_date  no_guests room_category  \\\n",
       "0     19-04-22    2022-05-10     12/5/2022        2.0           RT1   \n",
       "1     8/5/2022    2022-05-10      15-05-22        2.0           RT1   \n",
       "2     5/5/2022    2022-05-10     11/5/2022        2.0           RT1   \n",
       "3     5/5/2022    2022-05-10     11/5/2022        1.0           RT1   \n",
       "4     9/5/2022    2022-05-10      15-05-22        4.0           RT1   \n",
       "\n",
       "  booking_platform  ratings_given booking_status  revenue_generated  \\\n",
       "0           others            5.0    Checked Out               9100   \n",
       "1           others            NaN      Cancelled               9100   \n",
       "2     makeyourtrip            NaN      Cancelled               9100   \n",
       "3     makeyourtrip            5.0    Checked Out               9100   \n",
       "4    direct online            5.0    Checked Out              10920   \n",
       "\n",
       "   revenue_realized property_name category   city  \n",
       "0              9100  Atliq Grands   Luxury  Delhi  \n",
       "1              3640  Atliq Grands   Luxury  Delhi  \n",
       "2              3640  Atliq Grands   Luxury  Delhi  \n",
       "3              9100  Atliq Grands   Luxury  Delhi  \n",
       "4             10920  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings=pd.merge(df_date,df_updated_bookings,left_on='date',right_on='check_in_date')\n",
    "df_updated_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "6f03b866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jul 22    548606876\n",
       "May 22    548606876\n",
       "Jun 22    530909880\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('mmm yy')['revenue_realized'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1605659b",
   "metadata": {},
   "source": [
    "8. Print revenue realized per hotel type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "7e2cc2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Exotica    306727356\n",
       "Atliq Palace     291245044\n",
       "Atliq City       268871380\n",
       "Atliq Bay        265252284\n",
       "Atliq Blu        254634012\n",
       "Atliq Grands     181085716\n",
       "Atliq Seasons     60307840\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('property_name')['revenue_realized'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b24513d",
   "metadata": {},
   "source": [
    "9. Print average rating per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "132b261f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Mumbai       3.791667\n",
       "Hyderabad    3.709459\n",
       "Delhi        3.666667\n",
       "Bangalore    3.319149\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('city')['ratings_given'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4891d3ee",
   "metadata": {},
   "source": [
    "10. Print a pie chart of revenue realized per booking platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "78a7ed76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            669538924\n",
       "makeyourtrip      328378084\n",
       "direct online     177791380\n",
       "logtrip           150826916\n",
       "tripster          118820668\n",
       "journey            92043424\n",
       "direct offline     90724236\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('booking_platform')['revenue_realized'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "cd9b64d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='revenue_realized'>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_updated_bookings.groupby('booking_platform')['revenue_realized'].sum().sort_values(ascending=False).plot(kind='pie')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}